Industry & Manufacturing
Drive smarter operations, faster innovation, and stronger outcomes in manufacturing—by securely connecting edge devices and industrial machinery, all while minimizing data transfer and communication overhead.
Driving innovation in industry & manufacturing through secure, privacy-preserving collaboration
Sherpa.ai empowers manufacturers to harness the full potential of Industry 4.0 by enabling secure, collaborative AI across factories, suppliers, and edge devices—without ever sharing sensitive operational data. From predictive maintenance and quality control to supply chain optimization.
Sherpa.ai’s Federated Learning platform reduces communication overhead, ensures data sovereignty, and delivers real-time insights across decentralized environments.
Sherpa.ai Federated Learning Platform will enable us to make better decisions while protecting privacy and security using cutting age technologies like Secure Multipart Computation. This platform will enable greater sharing of data between organizations while preserving trust, privacy and security.
Former Deputy Director, Office in Science and Technology Policy, The White House
Federated Learning Cases in Industry & Manufacturing
Collaborative Predictive Maintenance
Optimize operations across production plants without sharing sensitive internal information.
Supply Chain Optimization
Improve efficiency across the entire logistics network while preserving each participant’s data privacy.
AI-Driven Product Quality
Detect production defects using AI models trained on distributed data with no risk of IP leakage.
Collaborative Development Among Manufacturers
Accelerate innovation through joint learning without disclosing proprietary data.
Predictive Maintenance for Aircraft Engines with Federated Learning
This project demonstrates the value of Sherpa.ai to improve prediction of aircraft engine failures —without transferring any raw sensor data across systems.
In certain safety-critical environments like aerospace, data centralization is often infeasible due to regulatory, privacy, and communication limitations. Sherpa.ai addresses these challenges by enabling collaborative AI model training directly at the edge—keeping data on-device and only exchanging encrypted model parameters.
Results show that the federated models can match the performance of centralized training while significantly outperforming isolated, single-node models. Moreover, the solution reduces data transfer overhead, protects proprietary data, and improves energy efficiency—critical for real-time, resource-constrained environments like satellites, aircrafts, or remote fleets.
This case study demonstrate how Sherpa.ai’s platform meets the technical, regulatory, and operational demands of predictive maintenance in Industry 4.0 and aerospace scenarios—delivering accurate, scalable, and privacy-preserving AI at the edge
Federated Malware Detection Across Organizations with Sherpa.ai
This project demonstrates the value of Sherpa.ai’s Federated Learning platform in detecting ransomware across distributed networks —without transferring any raw telemetry or behavioral data between organizations.
In cybersecurity settings, particularly for malware and ransomware detection, centralizing data from multiple entities poses major risks and challenges, including regulatory barriers (e.g., GDPR), loss of control over sensitive system logs, and high communication costs. Sherpa.ai addresses these issues with a privacy-preserving architecture that allows organizations—such as banks, healthcare providers, or smart factories—to collaboratively train malware detection models while keeping data local.
Results show that the federated model significantly outperforms isolated (local-only) models and approaches the performance of centralized training.
This case study demonstrates how Sherpa.ai’s platform meets operational demands of real-world cybersecurity scenarios—delivering scalable, accurate, and privacy-preserving AI for proactive threat detection across organizational boundaries.
